An Extended Result on the Optimal Estimation Under the Minimum Error Entropy Criterion
AbstractThe minimum error entropy (MEE) criterion has been successfully used in fields such as parameter estimation, system identification and the supervised machine learning. There is in general no explicit expression for the optimal MEE estimate unless some constraints on the conditional distribution are imposed. A recent paper has proved that if the conditional density is conditionally symmetric and unimodal (CSUM), then the optimal MEE estimate (with Shannon entropy) equals the conditional median. In this study, we extend this result to the generalized MEE estimation where the optimality criterion is the Renyi entropy or equivalently, the α-order information potential (IP). View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Chen, B.; Wang, G.; Zheng, N.; Principe, J.C. An Extended Result on the Optimal Estimation Under the Minimum Error Entropy Criterion. Entropy 2014, 16, 2223-2233.
Chen B, Wang G, Zheng N, Principe JC. An Extended Result on the Optimal Estimation Under the Minimum Error Entropy Criterion. Entropy. 2014; 16(4):2223-2233.Chicago/Turabian Style
Chen, Badong; Wang, Guangmin; Zheng, Nanning; Principe, Jose C. 2014. "An Extended Result on the Optimal Estimation Under the Minimum Error Entropy Criterion." Entropy 16, no. 4: 2223-2233.